Image recognition makes it possible to detect what is present on a photograph or a picture. The photograph or a picture may include a text, a number or any animation and the like. Traditionally, there are various image-matching techniques for image recognition, which includes, though not limited to shape matching, object matching, and pattern matching.
Conventionally, an optical character recognition (OCR) technique is used for recognizing the texts within an Image-, the image recognition and pattern matching in many applications like number plate recognition. Apart from various advantages there are some major disadvantages in the technique. One of the major disadvantages with the technique is that it does not work efficiently due to a variation in the painting style of a number plate. To incorporate all variations in painting style, there is a need to learn the OCR system with all possible fonts/painting style of the characters. Further, there are no standard fonts and size of the number plates, which can impose a restriction on the OCR technique. Moreover, there are different painting agents/agencies who write the numbers on the plates with different and unique styles that make it very difficult for the OCR technique to recognize the exact image.
There are various other some commercially available applications like Google Goggles that recognizes images and text captured by a device like mobile or cameras. Additionally, there are various limitations and challenges for the applications including the need of the application to require the Internet Connection continuously. Further, it is required to extract individual characters on embedded mobile platform which has constraints with respect to both memory and processor. Furthermore, the presence of specular reflection makes the recognition task more difficult; images captured are mostly in the night, so the images obtained are often blurred and have low contrast and text localization against the background of the image captured.